Physician-Patient Active Learning Base Communication Method and System

Abstract

A system intelligently implements communication between at least one patient and at least one medical practitioner, preferably using two-way natural language conversation. The patient discloses or is prompted to disclose relevant medical condition and background, daily routines, that a medical practitioner would want to diagnose and prescribe for a present medical condition. The conversation is content-based routed in real-time to multiple potential medical responders, preferably including a machine learning (ML) based software agent, as well as human responders having various levels of medical expertise. Such routing advantageously minimizes volume of irrelevant information sent to a responder, and also reduces the cost of information responded to by the most appropriate responder. Over time, the ML-based software agent improves performance by using training data from patient-system communications, and/or via active learning methods. Compartmentalization of patient information promotes patient privacy policies while maximizing relevance of patient-system

What is claimed is: 
     
         1 . A system enabling diagnostic communications between a least one patient and at least one medical practitioner provider of medical care, the system including:
 a computer system having at least a processor, memory, and at least one software routine stored in said memory and executable by said processor to implement said system;   a responder system, coupled to said computer system, including at least a machine learning (ML) based responder, a first human responder having a first level of medical expertise, and a second human responder having a second, higher, level of medical expertise;   a natural language understanding (NLU) system, coupled to said computer system and to said responder system, said NLU system interfacing spoken communication between said at least one patient and said responder system;   means for triaging medical importance of spoken communication from said at least one patient, triaging coupled to said NLU system and to said responder system;
 wherein said means for triaging directs said spoken communication to a chosen responder in said responder system selected from a group consisting of said ML based responder, said first human responder, and said second human responder; 
 wherein said chosen responder responds to said at least one patient in natural language using said NLU system. 
   
     
     
         2 . The system of  claim 1 , further including:
 memory storing at least a patient state record containing a full medical history of said at least one patient;   wherein said means for triaging is coupled to said memory storing at least a patient state record, and uses contents therein in choosing an appropriate responder from said responder system.   
     
     
         3 . The system of  claim 1 , further including a knowledge base memory, containing general knowledge of at least one of known medical symptoms, commonly selected medical treatments, common prognosis for said known medical symptoms;
 wherein said knowledge base memory is accessible to at least one of said computer system, said NLU system, said responder system, and said means for triaging.   
     
     
         4 . The system of  claim 1 , wherein if said machine learning (ML) based responder determines a present complaint by said at least one patient indicates an emergency condition, said system instructs said at least one patient to immediately dial 9-1-1. 
     
     
         5 . The system of  claim 1 , wherein said machine learning (ML) based responder is implemented using artificial intelligence. 
     
     
         6 . The system of  claim 1 , wherein said responder system includes at least one of a human telephone operator, a human licensed nurse, and a human licensed physician. 
     
     
         7 . The system of  claim 1 , wherein said spoken communication is in the form of textual communication 
     
     
         8 . The system of  claim 1 , further including a proactive/background sub-system. 
     
     
         9 . The method of  claim 1  where the method of active learning is employed to continually improve the MKB to improve the percentage of the conversation handle that exceeds the standard care provided at a medical facility. 
     
     
         10 . The method of  claim 1  where the user-state database anonymizes patient historic data. 
     
     
         11 . The method of  claim 1  where the response is of the form of one of at least a reply, a question, an information or an informative piece relevant to the present conversation. 
     
     
         12 . The method of  claim 1  where information in incrementally collected and processed in an authoritative manner as opposed to patient seeking such information via search engines and forums. 
     
     
         13 . The method of  claim 3  where the algorithms moderate the conversation with human and augments is with information to reduce time and result in higher reliability in medical domain. 
     
     
         14 . The method of  claim 3  where at least 90% of conversation is resolved at L0 (ML module). 
     
     
         15 . The method of  claim 3  where if the conversation is not resolved in any level the conversation escalates to higher layers. 
     
     
         16 . The method of  claim 3 , where L0 is a pure machine learning module 
     
     
         17 . The method of  claim 3 , where L1 is an operator assisted with ML modules, MKB, and user history data. 
     
     
         18 . The method of  claim 3 , where L2 is a medical professional assisted with ML modules, MKB, and user history data. 
     
     
         19 . The method of  claim 3 , where L1 is a medical doctor assisted with ML modules, MKB, and user history data.